Global Energy Trends

INFO 523 - Project Final

The goal of this project is to analyze the complex relationships between economic and population growth, sustainable energy practices, and energy consumption

Author

Affiliation

data detectives - Ayesha, Abhishek, Sheemithra, Toluwanimi, Valerie, Alyssa

School of Information, University of Arizona

Abstract

This project utilizes the comprehensive energy dataset from Our World in Data, spanning from 1900 to 2022, to examine the global energy consumption trends regarding economic growth, population dynamics, and the adoption of sustainable energy practices. The primary goal of the project is to design a predictive dashboard that models a nation’s energy consumption based on essential factors such as population size, GDP, and the proportion of electricity derived from renewable sources. The analysis will utilize a range of statistical and machine learning techniques, including time series decomposition, linear regression for key predictors, and regression analysis. We will evaluate the performance of these regression models using R-squared and Root Mean Squared Error (RMSE) metrics to gauge their accuracy and explanatory power. This evaluation is essential for enhancing predictive accuracy and reliability in energy policy formulation and planning. The project will analyze trends in the use of renewable energy at the regional and national levels, with a certain emphasis on emphasizing countries that lead the way in sustainable energy practices and those making progress toward lower greenhouse gas emissions. This analysis will provide crucial insights for industry and researchers dedicated to promoting energy sustainability and promoting economic growth.

Question 1

Is it possible to predict a nation’s power consumption by considering its population size, gross domestic product (GDP), and the percentage of electricity generated from renewable sources and changes across the years?

A right-skewed distribution is shown by the distribution’s shape, which shows that most values are low and that frequency drops off quickly as values rise. In order to train the model to identify patterns and to validate its correctness by contrasting the projected values with the actual data shown in the plot, the historical data represented in this histogram would be crucial.

The relationship between primary energy consumption and the GDP, population size, and the proportion of power derived from renewable sources is represented graphically in this plot. If persistent patterns are seen over time, one might utilize the spread and trends of the scatter points to deduce that there might be correlations between these parameters and a country’s power consumption, which could be used to anticipate energy usage.

Indicating possible relationships between these variables and a country’s power consumption, the graphic shows how the population, GDP, percentage of renewable electricity, and energy consumption have changed over time. For example, if GDP and population growth are accompanied by an upward trend in the primary energy consumption curve, this could indicate that energy demand is driven by economic activity and demographic growth. On the other hand, while a rise in the proportion of power derived from renewable sources may not necessarily translate into reduced energy usage, it may suggest a change in the composition of energy sources. Time periods in which the growth of energy consumption slows down or deviates from trends in GDP and population may be linked to advancements in energy efficiency or structural adjustments in the economy. examining these patterns and connections across time. In order to forecast future power consumption patterns, data demonstrating a high historical association between these variables can be used to develop a predictive model through the analysis of such trends and relationships across time.

Question 2

What countries or regions are engaging in sustainable energy practices and relying more on renewable energy compared to nonrenewable energy? Which countries are moving towards the trajectory of relying more on renewable energy and producing less greenhouse gas emissions?

Data Wrangling for Density Plot

Density plots for renewable and non-renewable energy for the continents

Renewables Consumption Plot

Visualizing the density plots for renewable consumption

Non-renewables consumption Plot

The visualization for the non-renewable consumption of energy

Repo Organization

The following folders comprise the project repository

.github/: This directory is designated for files associated with GitHub, encompassing workflows, actions, and templates tailored for issues.
_extra/: Reserved for miscellaneous files that don’t neatly fit into other project categories, providing a catch-all space for various supplementary documents.
_freeze/: Within this directory lie frozen environment files containing comprehensive information regarding the project’s environment configuration and dependencies.
data/: Specifically allocated for storing i data files crucial for the project’s functionality, encompassing input files, datasets, and other essential data resources.
images/: Serving as a repository for visual assets employed throughout the project, including diagrams, charts, and screenshots, this directory maintains visual elements integral to project documentation and presentation.
.gitignore: This file functions to specify exclusions from version control, ensuring that designated files and directories remain untracked by Git, thus streamlining the versioning process.
README.md: Serving as the primary hub of project information, this README document furnishes essential details encompassing project setup, usage instructions, and an overarching overview of project objectives and scope.
_quarto.yml: Acting as a pivotal configuration file for Quarto, this document encapsulates various settings and options governing the construction and rendering of Quarto documents, facilitating customization and control over document output.
about.qmd: This Quarto Markdown file supplements project documentation by providing additional contextual information, elucidating project purpose, contributor insights, and other pertinent project details.
index.qmd: index.qmd: This serves as the main documentation page for our project. This Quarto Markdown file provides detailed descriptions of our project, including all code and visualization.

--- title: "Global Energy Trends" subtitle: "INFO 523 - Project Final" author: - name: "data detectives - Ayesha, Abhishek, Sheemithra, Toluwanimi, Valerie, Alyssa" affiliations: - name: "School of Information, University of Arizona" description: "The goal of this project is to analyze the complex relationships between economic and population growth, sustainable energy practices, and energy consumption" format: html: code-tools: true code-overflow: wrap embed-resources: true editor: visual execute: warning: false echo: false jupyter: python3 --- ## Abstract This project utilizes the comprehensive energy dataset from Our World in Data, spanning from 1900 to 2022, to examine the global energy consumption trends regarding economic growth, population dynamics, and the adoption of sustainable energy practices. The primary goal of the project is to design a predictive dashboard that models a nation’s energy consumption based on essential factors such as population size, GDP, and the proportion of electricity derived from renewable sources. The analysis will utilize a range of statistical and machine learning techniques, including time series decomposition, linear regression for key predictors, and regression analysis. We will evaluate the performance of these regression models using R-squared and Root Mean Squared Error (RMSE) metrics to gauge their accuracy and explanatory power. This evaluation is essential for enhancing predictive accuracy and reliability in energy policy formulation and planning. The project will analyze trends in the use of renewable energy at the regional and national levels, with a certain emphasis on emphasizing countries that lead the way in sustainable energy practices and those making progress toward lower greenhouse gas emissions. This analysis will provide crucial insights for industry and researchers dedicated to promoting energy sustainability and promoting economic growth. # Question 1 Is it possible to predict a nation's power consumption by considering its population size, gross domestic product (GDP), and the percentage of electricity generated from renewable sources and changes across the years? ```{python} #| label: question #| echo: false # cell library import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import statsmodels.api as sm from statsmodels.tsa.seasonal import seasonal_decompose from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import matplotlib.dates as mdates # Load the dataset data = pd.read_csv('data/owid-energy-data.csv') #columns to keep keep = (['year', 'population', 'gdp', 'electricity_generation', 'primary_energy_consumption', 'renewables_electricity']) #data for q1 q1_data = data[keep] # Drop rows with any empty values q1_data_cleaned = q1_data.dropna() # Save the cleaned dataset to a new CSV file q1_data_cleaned.to_csv('data/q1_energy_data_cleaned.csv', index = False) ``` ```{python} #| label: question1 #| echo: false # Load the clean dataset data = pd.read_csv('data/q1_energy_data_cleaned.csv') # Calculate the percentage of electricity generated from renewable sources data['renewables_percentage'] = (data['renewables_electricity'] / data['electricity_generation']) * 100 columns_to_normalize = ['electricity_generation', 'primary_energy_consumption', 'renewables_electricity'] for column in columns_to_normalize: data[column] = (data[column] - data[column].min()) / (data[column].max() - data[column].min()) # Save the updated dataset to a new CSV file data.to_csv('data/q1_energy_data_processed.csv', index = False) ``` ```{python} #| label: question2 #| echo: false # Load the processed dataset data = pd.read_csv('data/q1_energy_data_processed.csv') plt.figure(figsize=(10, 8)) sns.histplot(data['primary_energy_consumption'], kde = True) plt.title('Distribution of Target Variable "primary_energy_consumption"') plt.xlabel('Primary Energy Consumption') plt.ylabel('Frequency') plt.xlim(0, 0.065) plt.ylim(0, 90) plt.show() ``` A right-skewed distribution is shown by the distribution's shape, which shows that most values are low and that frequency drops off quickly as values rise. In order to train the model to identify patterns and to validate its correctness by contrasting the projected values with the actual data shown in the plot, the historical data represented in this histogram would be crucial. ```{python} #| label: question3 #| echo: false # Create a dictionary that maps feature names to desired labels feature_labels = { 'population': 'Population (in millions)', 'gdp': 'GDP (in millions)', 'renewables_electricity': 'Renewable Electricity' } # Set up the plot area to have 1 row and 3 columns fig, axes = plt.subplots(1, 3, figsize=(12, 6)) for i, column in enumerate(feature_labels): if column in ['population', 'gdp']: sns.scatterplot(ax = axes[i], x = data[column] / 1e6, y = 'primary_energy_consumption', data = data, color = 'green') axes[i].set_xlabel(feature_labels[column]) else: sns.scatterplot(ax = axes[i], x = column, y = 'primary_energy_consumption', data = data, color = 'green') axes[i].set_xlabel(feature_labels[column]) # Remove the y-axis label for individual plots and y-tick labels for a cleaner look axes[i].set_ylabel('') axes[i].set_yticklabels([]) # Adding a common y-axis label on the left side of the subplots fig.text(0.00, 0.5, 'Primary Energy Consumption', va = 'center', rotation = 'vertical', fontsize = 9) # Adjust layout and add a common title plt.tight_layout(pad = 3.0, w_pad = 2.5, h_pad = 2.0) fig.suptitle('Scatter plot of Primary Energy Consumption vs Other Factors', fontsize = 12) plt.show() ``` The relationship between primary energy consumption and the GDP, population size, and the proportion of power derived from renewable sources is represented graphically in this plot. If persistent patterns are seen over time, one might utilize the spread and trends of the scatter points to deduce that there might be correlations between these parameters and a country's power consumption, which could be used to anticipate energy usage. ```{python} #| label: question4 #| echo: false #| warning: false # Load and preprocess the data data = pd.read_csv('data/q1_energy_data_processed.csv') data['year'] = pd.to_datetime(data['year'], format = '%Y') data.set_index('year', inplace = True) # Create the plot with customized settings fig, ax1 = plt.subplots(figsize = (12, 6)) # Plotting with different line styles and markers ax1.plot(data.index, data['population'], label = 'Population', color = 'blue', linestyle = '-', marker = 'o', linewidth = 1) ax1.plot(data.index, data['gdp'], label='GDP', color='red', linestyle='--', marker='x', linewidth=1) ax2 = ax1.twinx() ax2.plot(data.index, data['renewables_electricity'], label = 'Renewables Electricity', color = 'green', linestyle = '-.', marker = '^', linewidth = 1, alpha = 0.7) ax2.plot(data.index, data['primary_energy_consumption'], label = 'Primary Energy Consumption', color = 'purple', linestyle = ':', marker = 's', linewidth = 1, alpha = 0.7) # Configure the x-axis with date formatting ax1.xaxis.set_major_locator(mdates.YearLocator(3)) ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) # Labeling and titles ax1.set_xlabel('Year') ax1.set_ylabel('Population and GDP') ax2.set_ylabel('Renewables and Energy Consumption') ax1.set_title('Time Series of Population, GDP, Renewable Electricity, and Energy Consumption') # Combine legends from both axes lines, labels = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines + lines2, labels + labels2, loc='upper left') plt.xticks(rotation = 45) # Adjust layout plt.tight_layout(pad = 5.0, w_pad = 1.5, h_pad = 1.0) plt.show() ``` Indicating possible relationships between these variables and a country's power consumption, the graphic shows how the population, GDP, percentage of renewable electricity, and energy consumption have changed over time. For example, if GDP and population growth are accompanied by an upward trend in the primary energy consumption curve, this could indicate that energy demand is driven by economic activity and demographic growth. On the other hand, while a rise in the proportion of power derived from renewable sources may not necessarily translate into reduced energy usage, it may suggest a change in the composition of energy sources. Time periods in which the growth of energy consumption slows down or deviates from trends in GDP and population may be linked to advancements in energy efficiency or structural adjustments in the economy. examining these patterns and connections across time. In order to forecast future power consumption patterns, data demonstrating a high historical association between these variables can be used to develop a predictive model through the analysis of such trends and relationships across time. # Question 2 What countries or regions are engaging in sustainable energy practices and relying more on renewable energy compared to nonrenewable energy? Which countries are moving towards the trajectory of relying more on renewable energy and producing less greenhouse gas emissions? ```{python} #| label: MAP1 #| echo: false #| warning: false import pandas as pd import geopandas as gpd import plotly.express as px # Loading data data = pd.read_csv("data/owid-energy-data.csv") world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Merging world shapefile with energy data world = world.merge(data, how='left', left_on='iso_a3', right_on='iso_code') # Droping NaN values from columns used in calculations columns_to_drop_na = ['solar_consumption', 'wind_consumption', 'hydro_consumption', 'other_renewable_consumption', 'energy_per_capita'] # Filtering data from 2000 to 2023 world = world[(world['year'] >= 2000) & (world['year'] <= 2023)] # Grouping by year and country, summing renewable energy consumption and energy per capita world = world.groupby(['iso_a3', 'year', 'name']).agg({ 'solar_consumption': 'sum', 'wind_consumption': 'sum', 'hydro_consumption': 'sum', 'other_renewable_consumption': 'sum', 'energy_per_capita': 'mean' }).reset_index() # Calculating total renewable energy consumption world['total_renewable_consumption'] = world['solar_consumption'] + world['wind_consumption'] + world['hydro_consumption'] + world['other_renewable_consumption'] # Calculate renewable energy share world['renewable_energy_share'] = (world['total_renewable_consumption'] / world['energy_per_capita']) * 100 # Setting year column as date world['year'] = pd.to_datetime(world['year'], format='%Y') # Plotting the animated map fig = px.choropleth(world, locations='iso_a3', color='renewable_energy_share', hover_name='name', hover_data={'iso_a3': False, 'renewable_energy_share': True}, animation_frame=world['year'].dt.year, range_color=(0, 7), projection='natural earth', color_continuous_scale=px.colors.sequential.Plasma, title='Share of Renewable Energy Consumption (%)') # Setting x-axis format to display only the year fig.update_xaxes(dtick='M1', tickformat='%Y') fig.show() ``` ```{python} #| label: BAR5 #| echo: false #| warning: false import pandas as pd import geopandas as gpd import plotly.graph_objects as go world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Merging world shapefile with energy data world = world.merge(data, how='left', left_on='iso_a3', right_on='iso_code') # Droping NaN values from columns used in calculations columns_to_drop_na = ['solar_consumption', 'wind_consumption', 'hydro_consumption', 'other_renewable_consumption', 'energy_per_capita'] # Filtering data from 2000 to 2023 world = world[(world['year'] >= 2000) & (world['year'] <= 2023)] # Grouping by year and country, summing renewable energy consumption and energy per capita world = world.groupby(['iso_a3', 'year', 'name']).agg({ 'solar_consumption': 'sum', 'wind_consumption': 'sum', 'hydro_consumption': 'sum', 'other_renewable_consumption': 'sum', 'energy_per_capita': 'mean' }).reset_index() # Calculating total renewable energy consumption world['total_renewable_consumption'] = world['solar_consumption'] + world['wind_consumption'] + world['hydro_consumption'] + world['other_renewable_consumption'] # Calculate renewable energy share world['renewable_energy_share'] = (world['total_renewable_consumption'] / world['energy_per_capita']) * 100 # Setting year column as date world['year'] = pd.to_datetime(world['year'], format='%Y') # Calculate mean renewable energy share by country country_stats = world.groupby('name')['renewable_energy_share'].mean().reset_index() # Identify top 10 countries with the highest mean renewable energy share top_10_countries = country_stats.nlargest(10, 'renewable_energy_share')['name'].tolist() # Filter data for top 10 countries top_10_world = world[world['name'].isin(top_10_countries)] # Sort data by renewable energy share top_10_world = top_10_world.sort_values(by='renewable_energy_share', ascending=False) # Plotting the animated bar plot with Plotly Graph Objects fig = go.Figure() # Create bar traces for each year for year, df in top_10_world.groupby('year'): fig.add_trace(go.Bar( x=df['name'], y=df['renewable_energy_share'], name=str(year.year), hoverinfo='x+y', hovertemplate='%{x} Renewable Energy Share: %{y:.2f}%<extra></extra>', visible=False if year != top_10_world['year'].min() else True, marker=dict(color='rgba(50, 171, 96, 0.6)') )) # Add play button and slider fig.update_layout( updatemenus=[dict( type="buttons", buttons=[dict(label="Play", method="animate", args=[None, {"frame": {"duration": 500, "redraw": True}, "fromcurrent": True, "transition": {"duration": 300, "easing": "quadratic-in-out"}}] )] )], title='Top 10 Countries with Highest Mean Renewable Energy Share', xaxis=dict(title='Country'), yaxis=dict(title='Mean Renewable Energy Share (%)', range=[0, 20]) ) # Set initial layout fig.update_layout(showlegend=True,) # Create frames for each year frames = [go.Frame( data=[go.Bar( x=df['name'], y=df['renewable_energy_share'], name=str(year.year), hoverinfo='x+y', hovertemplate='%{x} Renewable Energy Share: %{y:.2f}%<extra></extra>', marker=dict(color='rgba(50, 171, 96, 0.6)') )], name=str(year.year) ) for year, df in top_10_world.groupby('year')] # Add frames to the figure fig.frames = frames fig.show() ``` ```{python} world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Merging world shapefile with energy data world = world.merge(data, how='left', left_on='iso_a3', right_on='iso_code') # Droping NaN values from columns used in calculations columns_to_drop_na = ['solar_consumption', 'wind_consumption', 'hydro_consumption', 'other_renewable_consumption', 'energy_per_capita'] world = world.dropna(subset=columns_to_drop_na) # Filtering data from 2000 to 2023 world = world[(world['year'] >= 2000) & (world['year'] <= 2023)] # Grouping by year and country, summing renewable energy consumption and energy per capita world = world.groupby(['iso_a3', 'year', 'name']).agg({ 'solar_consumption': 'sum', 'wind_consumption': 'sum', 'hydro_consumption': 'sum', 'other_renewable_consumption': 'sum', 'energy_per_capita': 'mean', 'greenhouse_gas_emissions': 'mean' }).reset_index() # Calculating total renewable energy consumption world['total_renewable_consumption'] = world['solar_consumption'] + world['wind_consumption'] + world['hydro_consumption'] + world['other_renewable_consumption'] # Calculating renewable energy share world['renewable_energy_share'] = (world['total_renewable_consumption'] / world['energy_per_capita']) * 100 # Setting year column as date world['year'] = pd.to_datetime(world['year'], format='%Y') # Calculating mean renewable energy share and greenhouse gas emissions by country country_stats = world.groupby('name').agg({ 'renewable_energy_share': 'mean', 'greenhouse_gas_emissions': 'mean' }).reset_index() # Identifing top countries with the highest mean renewable energy share top_renewable_countries = country_stats.nlargest(10, 'renewable_energy_share') # Identifing top countries with the lowest mean greenhouse gas emissions top_low_emissions_countries = country_stats.nsmallest(10, 'greenhouse_gas_emissions') # Creating bar plots fig_renewable = px.bar(top_renewable_countries, x='name', y='renewable_energy_share', labels={'renewable_energy_share': 'Mean Renewable Energy Share (%)'}, title='Top 10 Countries with Highest Mean Renewable Energy Share') fig_emissions = px.bar(top_low_emissions_countries, x='name', y='greenhouse_gas_emissions', labels={'greenhouse_gas_emissions': 'Mean Greenhouse Gas Emissions'}, title='Top 10 Countries with Lowest Mean Greenhouse Gas Emissions') # Showing plots fig_emissions.show() ``` ```{python} # Create renewable energy dataset as a subset of the data wind_data = data[['country', 'year', 'wind_consumption']] solar_data = data[['country', 'year', 'solar_consumption']] hydro_data = data[['country', 'year', 'hydro_consumption']] # remove all missing values wind_data_clean = wind_data.dropna() solar_data_clean = solar_data.dropna() hydro_data_clean = hydro_data.dropna() # Group the data by year and sum the wind consumption for each year across all countries grouped_wind_data = wind_data_clean.groupby('year')['wind_consumption'].sum() grouped_solar_data = solar_data_clean.groupby('year')['solar_consumption'].sum() grouped_hydro_data = hydro_data_clean.groupby('year')['hydro_consumption'].sum() # Plot each energy type with appropriate labels and colors plt.figure(figsize=(12, 10)) plt.plot(grouped_wind_data.index, grouped_wind_data.values, marker='o', color='skyblue', label='Wind') plt.plot(grouped_solar_data.index, grouped_solar_data.values, marker='o', color='goldenrod', label='Solar') plt.plot(grouped_hydro_data.index, grouped_hydro_data.values, marker='o', color='seagreen', label='Hydro') plt.xlabel('Year') plt.ylabel('Energy Consumption (in terawatt-hours)') plt.title('Global Renewable Energy Consumption') plt.grid(True) plt.legend() # Add a legend to differentiate the lines plt.tight_layout() # Adjust the plot to ensure everything fits without overlap plt.show() ``` ```{python} import matplotlib.pyplot as plt ``` ### Data Wrangling for Density Plot Density plots for renewable and non-renewable energy for the continents ```{python} #| label: data_wrang_density #| message: false #| warning: false ## loading the data set import pandas as pd energy = pd.read_csv('data/owid-energy-data.csv') ## performing data wrangling operations on the data set to make it suitable for the analysis ## selecting the necessary columns for this analysis energy2 = energy[['year','country','iso_code', 'biofuel_consumption', 'coal_consumption', 'fossil_fuel_consumption', 'gas_consumption', 'hydro_consumption', 'nuclear_consumption', 'oil_consumption', 'other_renewable_consumption', 'renewables_consumption', 'solar_consumption', 'wind_consumption']] ### removing the rows with only missing values in it with the exception of the year column energy2_cleaned = energy2.dropna(subset = energy2.columns.difference(['year']), how = 'all') ### we remove the rows in the country column that are not countries but rather regions or continents ### loading the data set for the country code country_codes = pd.read_csv('data/country_codes.csv') # filtering the energy data set by removing regions which are not countries from the country column in the data set ## first we select the valid country codes valid_country_codes = set(country_codes['Alpha-3 code']) ## filtering the country column in the data set energy_filtered = energy2_cleaned[energy2_cleaned['iso_code'].isin(valid_country_codes)] ###removing all rows with NA values except the iso_codes, year and country columns in the data set energy_wrangled = energy_filtered.dropna(subset = energy_filtered.columns.difference(['year', 'iso_code', 'country']), how = 'all') ### removing the values with na values in the data set energy_wrangled = energy_wrangled.fillna(0) # Select columns to check for 0 values columns_to_check = energy_wrangled.columns.difference(['year', 'iso_code', 'country']) # Filter rows with 0 values in all columns except 'year', 'country', and 'iso_code' energy_wrangled = energy_wrangled[(energy_wrangled[columns_to_check] != 0).any(axis = 1)] ### The next step is feature engineering where new columns in the data set are created ## note that there is a column for the renewable energy consumption total ### creating a column for the total consumption of non-renewable energy energy_wrangled['non_renewables_consumption'] = energy_wrangled['coal_consumption'] + energy_wrangled['fossil_fuel_consumption'] + energy_wrangled['gas_consumption'] + energy_wrangled['nuclear_consumption'] + energy_wrangled['oil_consumption'] ### rounding the rows in the newly column to 3 decimal places energy_wrangled['non_renewables_consumption'] = energy_wrangled['non_renewables_consumption'].round(3) ### a new column for the total consumption is created energy_wrangled['total_consumption'] = energy_wrangled['non_renewables_consumption'] + energy_wrangled['renewables_consumption'] #### making sure that the total consumption is in two decimal places energy_wrangled['total_consumption'] = energy_wrangled['total_consumption'].round(3) ### the next step is to feature engineer and group the data into continents ### generating a function that will group the countries into their respective continents def group_countries_by_continent(iso_code): continents = { 'Africa': ['DZA', 'AGO', 'BEN', 'BWA', 'BFA', 'BDI', 'CPV', 'CMR', 'CAF', 'TCD', 'COM', 'COG', 'COD', 'DJI', 'EGY', 'GNQ', 'ERI', 'SWZ', 'ETH', 'GAB', 'GMB', 'GHA', 'GIN', 'GNB', 'CIV', 'KEN', 'LSO', 'LBR', 'LBY', 'MDG', 'MWI', 'MLI', 'MRT', 'MUS', 'MAR', 'MOZ', 'NAM', 'NER', 'NGA', 'RWA', 'STP', 'SEN', 'SYC', 'SLE', 'SOM', 'ZAF', 'SSD', 'SDN', 'TZA', 'TGO', 'TUN', 'UGA', 'ZMB', 'ZWE'], 'Asia': ['AFG', 'ARM', 'AZE', 'BHR', 'BGD', 'BTN', 'BRN', 'KHM', 'CHN', 'CYP', 'GEO', 'IND', 'IDN', 'IRN', 'IRQ', 'ISR', 'JPN', 'JOR', 'KAZ', 'KWT', 'KGZ', 'LAO', 'LBN', 'MYS', 'MDV', 'MNG', 'MMR', 'NPL', 'PRK', 'OMN', 'PAK', 'PSE', 'PHL', 'QAT', 'SAU', 'SGP', 'KOR', 'LKA', 'SYR', 'TWN', 'TJK', 'THA', 'TLS', 'TKM', 'ARE', 'UZB', 'VNM', 'YEM', 'TUR', 'THA'], 'Europe': ['ALB', 'AND', 'AUT', 'BLR', 'BEL', 'BIH', 'BGR', 'HRV', 'CZE', 'DNK', 'EST', 'FIN', 'FRA', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL', 'ITA', 'XKX', 'LVA', 'LIE', 'LTU', 'LUX', 'MLT', 'MDA', 'MCO', 'MNE', 'NLD', 'MKD', 'NOR', 'POL', 'PRT', 'ROU', 'RUS', 'SMR', 'SRB', 'SVK', 'SVN', 'ESP', 'SWE', 'CHE', 'UKR', 'GBR', 'VAT'], 'North America': ['ATG', 'BHS', 'BRB', 'BLZ', 'CAN', 'CRI', 'CUB', 'DMA', 'DOM', 'SLV', 'GRL', 'GRD', 'GTM', 'HTI', 'HND', 'JAM', 'MEX', 'NIC', 'PAN', 'PRI', 'KNA', 'LCA', 'VCT', 'TTO', 'USA'], 'South America': ['ARG', 'BOL', 'BRA', 'CHL', 'COL', 'ECU', 'FLK', 'GUF', 'GUY', 'PRY', 'PER', 'SUR', 'URY', 'VEN'], 'Antarctica': ['ATA'], 'Europe': ['ALB', 'AND', 'AUT', 'BLR', 'BEL', 'BIH', 'BGR', 'HRV', 'CZE', 'DNK', 'EST', 'FIN', 'FRA', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL', 'ITA', 'XKX', 'LVA', 'LIE', 'LTU', 'LUX', 'MLT', 'MDA', 'MCO', 'MNE', 'NLD', 'MKD', 'NOR', 'POL', 'PRT', 'ROU', 'RUS', 'SMR', 'SRB', 'SVK', 'SVN', 'ESP', 'SWE', 'CHE', 'UKR', 'GBR', 'VAT'], 'Australia': ['AUS', 'NZL', 'PNG', 'FJI', 'SLB', 'VUT', 'NZL'] } for continent, countries in continents.items(): if iso_code in countries: return continent return None # Return None if ISO code not found in any continent # Apply the function to create the 'continents' column in energy_wrangled DataFrame energy_wrangled['continents'] = energy_wrangled['iso_code'].apply(group_countries_by_continent) ### removing hong kong since it is not a country from the data set energy_wrangled = energy_wrangled[energy_wrangled['iso_code'] != 'HKG'] ``` ### Renewables Consumption Plot Visualizing the density plots for renewable consumption ```{python} #| label: density1_redone #| output: false import matplotlib.pyplot as plt import seaborn as sns from matplotlib.animation import FuncAnimation from matplotlib.ticker import FuncFormatter from IPython.display import HTML # Extract unique years from the 'year' column years = energy_wrangled['year'].unique() # Function to format the x-axis labels with commas def format_with_commas(value, pos): return "{:,}".format(int(value)) # Function to update the plot for the selected year def update_plot(year): # Filter data for the selected year data_year = energy_wrangled[energy_wrangled['year'] == year] # Clear the previous plot plt.clf() # Plot the KDE plot for the selected year sns.kdeplot(data=data_year, x='renewables_consumption', hue='continents', multiple='stack', alpha=0.3, linewidth=0.2) # Set title plt.title(f'Density Plot of Renewables Consumption by Continent for Year {year}') # Set the x label plt.xlabel('Renewable Energy Consumption') # Set the x-axis tick formatter plt.gca().xaxis.set_major_formatter(FuncFormatter(format_with_commas)) # Initialize the plot with the first year update_plot(years[0]) # Create a FuncAnimation object without controls (slider, play/pause button) ani2 = FuncAnimation(plt.gcf(), update_plot, frames=years, interval=1000) # Convert the animation to HTML format html_animation2 = ani2.to_jshtml() ``` ```{python} #| label: hmtl_animation2 HTML(html_animation2) ``` ### Non-renewables consumption Plot The visualization for the non-renewable consumption of energy ```{python} #| label: density2_redone #| output: false import matplotlib.pyplot as plt import seaborn as sns from matplotlib.animation import FuncAnimation from matplotlib.ticker import FuncFormatter # Extract unique years from the 'year' column years = energy_wrangled['year'].unique() # Function to format the x-axis labels with commas def format_with_commas(value, pos): return "{:,}".format(int(value)) # Function to update the plot for the selected year def update_plot(year): # Filter data for the selected year data_year = energy_wrangled[energy_wrangled['year'] == year] # Clear the previous plot plt.clf() # Plot the KDE plot for the selected year sns.kdeplot(data=data_year, x='non_renewables_consumption', hue='continents', multiple='stack', alpha=0.3, linewidth=0.2) # Set title plt.title(f'Density Plot of Non-Renewables Consumption by Continent for Year {year}') # Set the x label plt.xlabel('Non-Renewable Energy Consumption') # Set the x-axis tick formatter plt.gca().xaxis.set_major_formatter(FuncFormatter(format_with_commas)) # Initialize the plot with the first year update_plot(years[0]) # Create a FuncAnimation object without controls (slider, play/pause button) ani = FuncAnimation(plt.gcf(), update_plot, frames=years, interval=1000) # Convert the animation to HTML format html_animation = ani.to_jshtml() # Display the HTML animation HTML(html_animation) ``` ```{python} #| label: html_animation HTML(html_animation) ``` # Repo Organization The following folders comprise the project repository - **.github/:** This directory is designated for files associated with GitHub, encompassing workflows, actions, and templates tailored for issues. - **\_extra/:** Reserved for miscellaneous files that don't neatly fit into other project categories, providing a catch-all space for various supplementary documents. - **\_freeze/:** Within this directory lie frozen environment files containing comprehensive information regarding the project's environment configuration and dependencies. - **data/:** Specifically allocated for storing i data files crucial for the project's functionality, encompassing input files, datasets, and other essential data resources. - **images/:** Serving as a repository for visual assets employed throughout the project, including diagrams, charts, and screenshots, this directory maintains visual elements integral to project documentation and presentation. - **.gitignore:** This file functions to specify exclusions from version control, ensuring that designated files and directories remain untracked by Git, thus streamlining the versioning process. - **README.md:** Serving as the primary hub of project information, this README document furnishes essential details encompassing project setup, usage instructions, and an overarching overview of project objectives and scope. - **\_quarto.yml:** Acting as a pivotal configuration file for Quarto, this document encapsulates various settings and options governing the construction and rendering of Quarto documents, facilitating customization and control over document output. - **about.qmd:** This Quarto Markdown file supplements project documentation by providing additional contextual information, elucidating project purpose, contributor insights, and other pertinent project details. - **index.qmd:** index.qmd: This serves as the main documentation page for our project. This Quarto Markdown file provides detailed descriptions of our project, including all code and visualization.